arXiv subjects

Dara Ron

Publications and source records attributed to Dara Ron.

6 recordsLinked to original sources

arXiv2026

SkyShare: Constellation-wide Sky Sharing for LEO-Radio Astronomy Coexistence

Rapidly growing low-Earth-orbit (LEO) constellations increasingly operate in the spectrum shared with radio astronomy services (RAS), creating escalating interference risks for sensitive scientific observations. Existing mitigation mechanisms rely on reactive beam steering, or avoidance near observatories but fail to account for aggregate sidelobe emissions-leading to residual interference and substantial, unnecessary capacity loss. We present SkyShare, a constellation-wide sky-sharing system that enables predictive, interference-aware spot beam scheduling to protect radio astronomy while preserving network coverage. SkyShare integrates high-fidelity orbital prediction with International Telecommunication Union (ITU)-compliant Equivalent Power Flux Density (EPFD) modeling, and real-time observatory data via Operational Data Sharing (ODS) to jointly optimize beam-cell assignments over observation windows. To make constellation-scale coordination tractable, we introduce a concept of EPFD-budgeted Region-of-Interest(RoI) that bounds residual sidelobe interference while confining optimization to a minimal, provably sufficient set of cells. Building on RoI, we formulate LEO-RAS coexistence as a scalable scheduling problem and design SkySched, a flow-based algorithm that is optimal in special cases and yields scalable near-optimal solutions in the general NP-hard setting. SkyShare operates entirely in the control plane and requires no satellite hardware changes. Using real Starlink constellation geometries, we evaluate SkyShare across 25 single-dish, Ku-band RAS sites worldwide. Compared to Starlink boresight avoidance, SkyShare reduces unserved cells by up to 90.68% while remaining within EPFD limits.

cs.NI↗

arXiv2026

TelcoAgent: A Scalable 5G Multi-KPM Forecasting With 3GPP-Grounded Explainability

Key Performance Measurement (KPM) forecasting is essential for proactive network management of 5G and next-generation telecom networks. However, existing machine learning (ML) approaches face significant limitations in scalability and explainability, restricting their effectiveness in real-world deployments. We propose TelcoAgent, a foundation model-based framework that enables accurate, scalable, and explainable forecasting of multiple KPMs across diverse network cells without the need for site-specific training. Specifically, the framework comprises three key components: (i) an automated three-agent pipeline that constructs a 3rd Generation Partnership Project (3GPP) knowledge graph directly from specification documents, (ii) a scalable, time-series foundation model (TSFM)-based prediction pipeline to deliver accurate, zero-shot forecasting, and finally (iii) a reasoning and explanation pipeline that provides actionable, domain-grounded diagnostics. Evaluated using a 3-month, real-world, city-scale 5G KPM dataset from a U.S.-based network operator, TelcoAgent demonstrates high forecasting accuracy for all 7 considered KPMs per cell across 200 cells, while delivering explainable insights and actionable instructions to address network degradations.

cs.AI↗

arXiv2026

OpenTwin: Closed-Loop Digital Twins for Trustworthy Policy Deployment in Open RAN

In open radio access networks (O-RAN), the near-real-time RAN Intelligent Controller (RIC) hosts third-party xApps whose training and validation risk disrupting the operational network. Disaggregation amplifies this risk, as no single party can certify a control action end to end. Indeed, our testbed shows an E2 control request reported as successful while the base station never applies the change. Digital twins (DTs) promise safe policy evaluation, yet existing O-RAN DTs largely rely on hand-crafted models, run without feedback from the deployment, and never say how often their predictions can be trusted. To fill this gap, we present OpenTwin, a closed-loop framework that learns the simulator configuration reproducing an operating deployment streamed measurements, certifies the resulting DT by re-simulation, calibrates it online, and evaluates each xApp action before it executes on the physical network. Every trust decision carries an error rate bounded by an operator-prescribed budget, with a drift detector limiting needless resynchronizations and a conformal fidelity gate admitting an action only when its predicted outcome range lies in the safe region. Extensive experiments across simulation and real-world testbeds confirm single-digit percentage error in reproduced measurements, false approvals an order of magnitude below every budget from 0.05 to 0.30, and a gated energy-saving xApp that retains roughly 40% of the saving achievable with perfect foresight.

cs.NI↗

arXiv2025

Jamming Smarter, Not Harder: Exploiting O-RAN Y1 RAN Analytics for Efficient Interference

The Y1 interface in O-RAN enables the sharing of RAN Analytics Information (RAI) between the near-RT RIC and authorized Y1 consumers, which may be internal applications within the operator's trusted domain or external systems accessing data through a secure exposure function. While this visibility enhances network optimization and enables advanced services, it also introduces a potential security risk -- a malicious or compromised Y1 consumer could misuse analytics to facilitate targeted interference. In this work, we demonstrate how an adversary can exploit the Y1 interface to launch selective jamming attacks by passively monitoring downlink metrics. We propose and evaluate two Y1-aided jamming strategies: a clustering-based jammer leveraging DBSCAN for traffic profiling and a threshold-based jammer. These are compared against two baselines strategies -- always-on jammer and random jammer -- on an over-the-air LTE/5G O-RAN testbed. Experimental results show that in unconstrained jamming budget scenarios, the threshold-based jammer can closely replicate the disruption caused by always-on jamming while reducing transmission time by 27\%. Under constrained jamming budgets, the clustering-based jammer proves most effective, causing up to an 18.1\% bitrate drop while remaining active only 25\% of the time. These findings reveal a critical trade-off between jamming stealthiness and efficiency, and illustrate how exposure of RAN analytics via the Y1 interface can enable highly targeted, low-overhead attacks, raising important security considerations for both civilian and mission-critical O-RAN deployments.

cs.CR↗

arXiv2025

Time-Dependent Network Topology Optimization for LEO Satellite Constellations

Today's Low Earth Orbit (LEO) satellite networks, exemplified by SpaceX's Starlink, play a crucial role in delivering global internet access to millions of users. However, managing the dynamic and expansive nature of these networks poses significant challenges in designing optimal satellite topologies over time. In this paper, we introduce the \underline{D}ynamic Time-Expanded Graph (DTEG)-based \underline{O}ptimal \underline{T}opology \underline{D}esign (DoTD) algorithm to tackle these challenges effectively. We first formulate a novel space network topology optimization problem encompassing a multi-objective function -- maximize network capacity, minimize latency, and mitigate link churn -- under key inter-satellite link constraints. Our proposed approach addresses this optimization problem by transforming the objective functions and constraints into a time-dependent scoring function. This empowers each LEO satellite to assess potential connections based on their dynamic performance scores, ensuring robust network performance over time without scalability issues. Additionally, we provide proof of the score function's boundary to prove that it will not approach infinity, thus allowing each satellite to consistently evaluate others over time. For evaluation purposes, we utilize a realistic Mininet-based LEO satellite emulation tool that leverages Starlink's Two-Line Element (TLE) data. Comparative evaluation against two baseline methods -- Greedy and $+$Grid, demonstrates the superior performance of our algorithm in optimizing network efficiency and resilience.

cs.NI↗

arXiv2024

Experimental Validation of a 3GPP Compliant 5G-Based Positioning System

The advent of 5G positioning techniques by 3GPP has unlocked possibilities for applications in public safety, vehicular systems, and location-based services. However, these applications demand accurate and reliable positioning performance, which has led to the proposal of newer positioning techniques. To further advance the research on these techniques, in this paper, we develop a 3GPP-compliant 5G positioning testbed, incorporating gNodeBs (gNBs) and User Equipment (UE). The testbed uses New Radio (NR) Positioning Reference Signals (PRS) transmitted by the gNB to generate Time of Arrival (TOA) estimates at the UE. We mathematically model the inter-gNB and UE-gNB time offsets affecting the TOA estimates and examine their impact on positioning performance. Additionally, we propose a calibration method for estimating these time offsets. Furthermore, we investigate the environmental impact on the TOA estimates. Our findings are based on our mathematical model and supported by experimental results.

eess.SY↗